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In the era of multiple global disruptions, firms are finding it to continue their business. MSMEs are impacted more as they have constrained resources. Organizational flexibility has emerged as an organizational and management principle that would help firms stay competitive even in volatile markets. This study aims to present a set of guidelines and insights for MSME managers to implement organizational flexibility in their organizations.

This study uses total interpretive structural modelling to study how the various factors contributing to organizational flexibility behave together. Behavioural theory is used to explain why organizations need to incorporate flexibility, and systems theory of organization is used to explain why an organization needs to have open boundaries.

Organizational flexibility is a principle that may be supported by the systems theory of organization. The study has shown that it is important for MSMEs to have supply chain collaborations to be more flexible. The study also shows pressure from competitors as the key driver that would make a firm more flexible, and that adequate support from management and technological skills are required to drive flexibility in an organization.

Single respondent bias may have occurred in this study. This can be eliminated by interviewing multiple people from the same organization. Further research around the reasoning for linkages can be explored with theory-driven grounded studies.

This study attempts to use a multi-criteria decision-making technique to present insights to managers to help them make their organizations flexible.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

This paper aims to analyse the current state of research to identify the link between Lean Manufacturing and Industry 4.0 (I4.0) technologies to map out different research themes, to uncover research gaps and propose key recommendations for future research, including lessons to be learnt from the integration of lean and I4.0.

A systematic literature review (SLR) is conducted to thematically analyse and synthesise existing literature on Lean Manufacturing–I4.0 integration. The review analysed 60 papers in peer-reviewed journals.

In total, five main research themes were identified, and a thematic map was created to explore the following: the relationship between Lean Manufacturing and I4.0; Lean Manufacturing and I4.0 implication on performance; Lean Manufacturing and I4.0 framework; Lean Manufacturing and I4.0 integration with other methodologies; and application of I4.0 technologies in Lean Manufacturing. Furthermore, various gaps in the literature were identified, and key recommendations for future directions were proposed.

The integration of Lean Manufacturing and I4.0 will eventually bring many benefits and offers superior and long-term competitive advantages. This research reveals the need for more analysis to thoroughly examine how this can be achieved in real life and promote operational changes that ensure enterprises run more sustainably.

The development of Lean Manufacturing and I4.0 integration is still in its infancy, with most articles in this field published in the past two years. The five main research themes identified through thematic synthesis are provided in the original contribution. This provides scholars better insight into the existing literature related to Lean Manufacturing and I4.0, further contributing to defining clear topics for future research opportunities. It also has important implications for industrialists, who can develop more profound and richer knowledge than Lean and I4.0, which would, in turn, help them develop more effective deployment strategies and have a positive commercial impact.

Based on a quantitative investigation, this paper endeavors to examine Industry 4.0 (I4.0) adoption process by studying the impacts of absorptive capacity (AC) and innovative ambidexterity (exploration, exploitation), while also considering the moderating influence of learning capability (LC).

Data has been gathered through administering questionnaire to 468 managers representing 175 manufacturing firms. Subsequently, PLS-SEM technique has been employed to verify the research hypotheses.

Study findings reveal that AC is significantly associated with I4.0 adoption and innovation ambidexterity. However, innovation ambidexterity demonstrates partial (only exploration) significant association with the adoption of I4.0. Similarly, the findings indicate that LC acts as a partial moderator between innovation ambidexterity (exploration) and I4.0 adoption.

The study presents significant insights into I4.0 adoption process. The findings may support managers of manufacturing firms to understand and assess the influence of integrating contextual factors facilitating successful adoption of I4.0. The study emphasizes necessity of managers’ awareness regarding the importance of firm’s AC to transform smoothly to I4.0 technologies. In addition to, encouraging the innovation ambidexterity along with LC to enhance the adoption of I4.0.

While researchers demonstrate increasing interest in applying I4.0, concrete evidence to support the I4.0 adoption process is, still, insufficient due to ongoing challenges in digital transformation. Consequently, further research is needed, particularly in exploring how a firm’s ability to realize knowledge and foster innovation contributes to implementing I4.0. This paper seeks to tackle this lack of research by examining the connection between AC, innovation ambidexterity, and LC and the adoption of I4.0 in an emerging economy.

The effectiveness of the Just-in-Time (JIT) production system in non-Japanese contexts is a topic of diverse findings. This study conducts a meta-analysis of empirical research on JIT and its relationship with performance, focusing on studies published since 1995. Additionally, it examines the moderating influence of National Culture (NC) values on JIT outcomes.

A total of 59 empirical studies with 211 effects and 17,008 observations from 18 countries are meta-analyzed. A meta-regression using hierarchical linear modeling (HLM) is performed to explore how four dimensions of National Culture (NC) moderate the impact. (viz. institutional collectivism, uncertainty avoidance future orientation, and power distance,) based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) culture model.

The meta-analysis results show that improved production efficiency, product quality and reduced wastes achieved through JIT deployments translate into the overall performance of organizations. The meta-regression results shed light on how local cultures influence the effectiveness of JIT across different countries.

The findings of meta-analysis have implications for multinational manufacturers in realizing efficacy of JIT. The research adds to the international operations management literature by examining how NC values influence strategies and decisions in operations management.

The purpose of this article is to conduct a bibliometric analysis of the cellular manufacturing system (CMS) literature published from 1982–2021 to identify key issues and trends for the future.

A six-stage methodology is used to conduct a literature review, which includes: (1) article collection; (2) inclusion/exclusion criteria; (3) reviewing the articles; (4) analyzing the articles; (5) framework development; and (6) future research directions. A total of 936 CMS-specific articles are reviewed. This paper made use of three software tools: the R package, VOSviewer and SciMAT.

According to the findings, the majority of CM researchers focused on cell formation and design. The USA, Iran and India are the top three leading publishers. Additionally, the gap and future direction of CM are discussed.

To the best of the authors' knowledge, the current study is the first attempt to investigate CMS evaluation through bibliometric and thematic analysis and provides a decisional framework as well as steps for CMS adoption. For individuals who are interested in understanding more about CMS and its evolution, this paper offers a starting point.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

Current flexible printed circuit (FPC) assembly relies heavily on manual labor, limiting capacity and increasing costs. Small FPC size makes automation challenging as terminals can be visually occluded. The purpose of this study is to use 3D tactile sensing to mimic human manual mating skills for enabling sensing offset between FPC terminals (FPC-t) and FPC mating slots (FPC-s) under visual occlusion.

The proposed model has three stages: spatial encoding, offset estimation and action strategy. The spatial encoder maps sparse 3D tactile data into a compact 1D feature capturing valid spatial assembly information to enable temporal processing. To compensate for low sensor resolution, consecutive spatial features are input to a multistage temporal convolutional network which estimates alignment offsets. The robot then performs alignment or mating actions based on the estimated offsets.

Experiments are conducted on a Redmi Note 4 smartphone assembly platform. Compared to other models, the proposed approach achieves superior offset estimation. Within limited trials, it successfully assembles FPCs under visual occlusion using three-axis tactile sensing.

A spatial encoder is designed to encode three-axis tactile data into feature maps, overcoming multistage temporal convolution network’s (MS-TCN) inability to directly process such input. Modifying the output to estimate assembly offsets with related motion semantics overcame MS-TCN’s segmentation points output, unable to meet assembly monitoring needs. Training and testing the improved MS-TCN on an FPC data set demonstrated accurate monitoring of the full process. An assembly platform verified performance on automated FPC assembly.

This comprehensive review study aims to analyze the current state of technology adoption (TA) within the textile industry, with a particular focus on the economic, environmental, and social dimensions.

Twenty-four articles from the Scopus database, spanning from January 2015 to March 2024, were meticulously selected for analysis. The review uses a qualitative approach, synthesizing diverse perspectives to provide a holistic understanding of TA in the textile sector.

The review highlights a noticeable lag in the adoption of new technologies, particularly in developing nations like India, within the textile industry. Despite significant technological advancements, there remains a gap between innovation and implementation. Sustainable approaches to mitigate environmental impacts emerge as a key focus, underscoring the need for operational enhancements and policy interventions.

The study’s reliance on articles from the SCOPUS database presents a limitation, potentially overlooking relevant research from other sources.

Practitioners in the textile industry can benefit from the review’s insights by understanding the importance of integrating technological advancements sustainably. By leveraging innovative solutions and collaborating with policymakers, firms can enhance operational efficiency while minimizing environmental impacts, thus ensuring long-term competitiveness.

Efforts to advance TA in the textile industry have significant social implications, including job creation, improved working conditions and reduced environmental harm.

Study addressed the insights for policymakers, industry practitioners and researchers seeking to drive technological innovation while addressing socio-economic and environmental challenges.

The COVID-19 epidemic caused significant disruptions to numerous supply chains. In order to enhance the resilience of supply chains, Collaboration (CO), Information Alignment (IA), and Big Data Analytics Capability (BDAC) have emerged as contemporary strategies within the humanitarian context. This study was conducted to explore the mechanism via which the effect of BDAC, IA and CO on Humanitarian Supply Chain Resilience (HSCR) in the humanitarian space could be optimized through Organizational Flexibility (OF).

A model of six hypotheses was developed based on the Organizational Information Processing Theory (OIPT). Data from 127 supply chain managers in humanitarian organizations were used to test the hypotheses. The analysis employed both descriptive and inferential statistics using SPSS version 26 and Smart-PLS version 3.

The study revealed that BDAC, IA, and CO individually influence supply chain resilience in the humanitarian setting while OF did not moderate the relationship between BDAC, IA, CO, and HSCR.

It is essential that humanitarian stakeholders prioritize factors that could increase supply chain resilience by employing contemporary BDA technologies, effective information flow, and collaborative strategies to set up a robust humanitarian SC system that could help lessen the impact of disasters.

This presents interesting insights that advance theoretical debates on how CO, IA, and BDAC under varying levels of OF could influence SCR in the humanitarian context. The paper further offers some useful guidance to managers in relief organizations who desire to build resilient supply chains by leveraging BDAC, collaboration and information alignment. Finally, the paper may also provoke future humanitarian scholars to replicate the study using different approaches.